Analysis of human footsteps utilizing multi-axial seismic fusion

This paper introduces a method of enhancing an unattended ground sensor (UGS) system's classification capability of humans via seismic signatures while subsequently discriminating these events from a range of other sources of seismic activity. Previous studies have been performed to consistently discriminate between human and animal signatures using cadence analysis. The studies performed herein will expand upon this methodology by improving both the success rate of such methods as well as the effective range of classification. This is accomplished by fusing multiple seismic axes in real-time to separate impulsive events from environmental noise. Additionally, features can be extracted from the fused axes to gather more advanced information about the source of a seismic event. Compared to more basic cadence determination algorithms, the proposed method substantially improves the detection range and correct classification of humans and significantly decreases false classifications due to animals and ambient conditions.